Modelling forager land-use by close-coupling

ABM and GIS

Institute of Archaeology

Mark Lake

Agenda

• What is agent-based GIS?

• Research problem

• Why take an agent-based approach?

• Overview of the model

• The palaeoenvironmental model

• The agent-based model

• Conclusions

What is agent-based GIS?

• Agent-based model

• An agent-based computersimulation implements acollection of (often interacting)artificial agents carrying outone or more tasks in anartificial environment

• Paradigmatic example isEpstein and Axtel’s 1996Sugarscape

• Benefits of integrating ABM and GIS

• GIS provides full range of tools for generating,updating and analysing the artificial environment

• Methods of integrating ABM and GIS

• Loose coupling

• 2 separate programs

• Close coupling

• 2 separate programs

• 1 program

What is agent-based GIS?

ABMGIS

ABMGIS

Research problem

• Archaeological problem

• The Southern HebridesMesolithic Project(directed by Steven Mithen)had obtained clear evidencethat Mesolithic people onIslay and Colonsay harvestedlarge numbers of hazelnuts

• Was the distribution of hazelnuts the primary determinant of land-use by relatively mobile foragers who sporadically visited the islands?

Why ABM?

• Traditional predictive model

• Assumes that foragers have complete knowledge of their environment

• Inductive, therefore identifies patterns, not necessarily causes

• Agent-based model

• Allows foragers to learn as they colonise islands

• Deductive, therefore tests a specific causal hypothesis (if used with care)

Overview of the model

Cognitive maps(GIS)

Environment(GIS)

Contains Ordnance Survey Data © Crown Copyright and database right 2011

Palaeoenvironmental model

• Requirement for ABM

• Input data and principles

• Quantitative model(mathematical and GIS)

• Validation

Requirement for ABM

• Model of the abundance of hazel

• Continuous surface

• Raster map(interpolatation from pointdata or theory driven?)

• Ethnographic scale

• 30km * 40km at50m x 50m resolution= 480,000 cells

Hazel

Input data and principles

• Pollen maps (Isochrone)

• Provide: coarse-grained information about regional presence or absence of species

• Environmental factors (soil, climate, etc.)

• Provide: information about potential of land to support a given species and therefore potential single species abundance

• Ecological principles (competition)

• Provide: information about extent to which potential species abundance will be realised

Paleoenvironmental model

• Presence or absence of species at 7000BP

• Previous broad scale reconstructions largely based on pollen isochrone maps

• McVean & Ratcliffe (1962) birch, oak/birch

• Bennett (1988) birch, oak, no trees > 200m

• Tipping (1994) birch/hazel/oak

• Edwards and Whittington (1997) birch/hazel/oak

• This model will include: oak, hazel, birch, ash

Paleoenvironmental model

• Environmentalfactors

• Land capability forwoodland

• Basic features of the postglacialclimate established by 7000BP

• Climate is a good ‘first sieve’

• Exposure is a proxy forwindthrow,droughtiness and wetness

• Final model combines climate andexposure (omits nutrients)

Paleoenvironmental model

• Single speciesabundance

• Mathematicalmodel

k = 1k = 1.4k = 2k = 4k = 8

b = 100a = 14

Tolerantspecies

b = 70a = 9

Intolerantspecies

k = 8

Paleoenvironmental model

• Adjusted speciesabundance

• Mathematicalmodel

• Succession = birch

hazel

oak

alder (in wet)

Paleoenvironmental model

• Adjusted speciesabundance

• GIS model

• Create single species models forbirch, hazel, oak, ash

• Combine using map algebra tomodel the effect of competition

Hazel

85% landcover

0 % landcover

85% landcover

0 % landcover

Hazel

Validation

• Relative species abundance

• Predicted by applying model to averageland capability within 5000m ofpollen cores

Landcapability

Sorn Valley

Loch a’ Bhogaidh

Loch Gorm

Agent-based model

Capable of reproductionCapable of reproduction

AutonomousAutonomous

Goal-directedGoal-directedSituatedSituated

RepresentationRepresentationof worldof world SocialSocial

ReactiveReactive

Cognitive maps(GIS)

Environment(GIS)

Agent environment

Digital elevation modelHazel abundance modelContains Ordnance Survey Data © Crown Copyright and database right 2011

Agent behaviour

• During the hazelnut season

• Agents forage forage around a base camp

• Agents return to a base camp at the end of each day

• The group moves the base camp at the end of each month

• Outside the hazelnut season

• The group disperses

• Agents move through the environment without reference to hazelnut abundance

Agent decision-making

• Agents

• Attempt to increase the energetic rate of return from foraging for hazelnuts

• Some are risk-takers, others are risk-averse(OFT problem of lost opportunity)

• Group

• Votes to move the base camp to the location which is expected to maximise the energetic rate of return from foraging for hazelnuts

Agent learning

• Individual learning

• Agents learn about theirenvironment as they movethrough it

• Each agent stores its currentknowledge in its ‘cognitive map’

• Cultural learning

• At the end of each day agentsshare information about their environment

• Effect is to update each agent’s individual cognitive map

Artefact depositionPrimary debitage

Secondary debitage

Microliths Scrapers

Base camp X X X X

Foraging for hazelnuts X X

Foraging outside hazelnut season X X

Experiments example 1

• Agents arrivingat Port Ellen

• Risk-taking agentsremain in south ofisland

All artefacts

Experiments example 2

• Agents arrivingat Port Askaig

• Risk-averse agents exploremore widely, but ultimatelymove to south of island

All artefacts

Experiments example 3

All artefacts

• Agents startingat Bolsay Farm

• Risk-averse foragers remainon the Rhinns of Islay

• Risk-taking foragers explorethe Rhinns, but ultimatelymove to south of island

Conclusion• In this case

• Distribution of hazelnuts was not the primary determinant of land-use by Mesolithic foragers visiting Islay, or the palaeoenvironmental model is wrong and/or the ABM assumptions are wrong

• More generally

• ABM shows that the degree of risk-taking and the degree of information sharing in the face of incomplete knowledge significantly affects the distribution of activity

• Static modelling is inappropriate for studying colonisation when the time taken to learn is long relative to the total occupation

Acknowledgements

Research reported here has been funded by:

Leverhulme Trust Special Research FellowshipNERC award GR3/9540 (to Prof. Mithen)